How to create a word cloud from a corpus in Python?
Each Answer to this Q is separated by one/two green lines.
From Creating a subset of words from a corpus in R, the answerer can easily convert a term-document matrix into a word cloud easily.
Is there a similar function from python libraries that takes either a raw word textfile or NLTK corpus or Gensim Mmcorpus into a word cloud?
The result will look somewhat like this:
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
stopwords = set(STOPWORDS)
def show_wordcloud(data, title = None):
wordcloud = WordCloud(
background_color="white",
stopwords=stopwords,
max_words=200,
max_font_size=40,
scale=3,
random_state=1 # chosen at random by flipping a coin; it was heads
).generate(str(data))
fig = plt.figure(1, figsize=(12, 12))
plt.axis('off')
if title:
fig.suptitle(title, fontsize=20)
fig.subplots_adjust(top=2.3)
plt.imshow(wordcloud)
plt.show()
show_wordcloud(Samsung_Reviews_Negative['Reviews'])
show_wordcloud(Samsung_Reviews_positive['Reviews'])
In case you require these word clouds for showing them in website or web app you can convert your data to json or csv format and load it to a JavaScript visualisation library such as d3. Word Clouds on d3
If not, Marcin’s answer is a good way for doing what you describe.
Example of amueller’s code in action
In command-line / terminal:
sudo pip install wordcloud
Then run python script:
## Simple WordCloud
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
text="all your base are belong to us all of your base base base"
def generate_wordcloud(text): # optionally add: stopwords=STOPWORDS and change the arg below
wordcloud = WordCloud(font_path="/Library/Fonts/Verdana.ttf",
width=800, height=400,
relative_scaling = 1.0,
stopwords = {'to', 'of'} # set or space-separated string
).generate(text)
fig = plt.figure(1, figsize=(8, 4))
plt.axis('off')
plt.imshow(wordcloud)
plt.axis("off")
## Pick One:
# plt.show()
plt.savefig("WordCloud.png")
generate_wordcloud(text)
here is the short code
#make wordcoud
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
stopwords = set(STOPWORDS)
def show_wordcloud(data, title = None):
wordcloud = WordCloud(
background_color="white",
stopwords=stopwords,
max_words=200,
max_font_size=40,
scale=3,
random_state=1 # chosen at random by flipping a coin; it was heads
).generate(str(data))
fig = plt.figure(1, figsize=(12, 12))
plt.axis('off')
if title:
fig.suptitle(title, fontsize=20)
fig.subplots_adjust(top=2.3)
plt.imshow(wordcloud)
plt.show()
if __name__ == '__main__':
show_wordcloud(text_str)
cv = CountVectorizer()
cvData = cv.fit_transform(DF["W"]).toarray()
cvDF = pd.DataFrame(data=cvData, columns=cv.get_feature_names())
cvDF["target"] = DF["T"]
def w_count(tar):
MO = cvDF[cvDF["target"] == tar].drop("target",axis=1)
x=[]
y=[]
for i in range(MO.shape[0]):
for j in cvDF.drop("target",axis=1):
if MO.iloc[i][j]>4:
x.append(j)
y.append(MO.iloc[i][j])
return x,y
for i in cvDF["target"]:
x,y = w_count(i)
plt.figure(figsize=(10, 6))
plt.title(i)
plt.xticks(rotation="vertical")
plt.bar(x,y)
plt.show()
for c in range(len(DF)):
w=[]
for i,j in zip(cvDF.T[c].index, cvDF.T[c].values):
a=[]
if j > 1:
a.append(i)
a.append(j)
w.append(a)
pd.DataFrame(w)
data = dict(w)
wc = WordCloud(width=800, height=400, max_words=200).generate_from_frequencies(data)
plt.figure(figsize=(10, 10))
plt.imshow(wc, interpolation='bilinear')
plt.axis('off')
plt.title(DF['T'][c])
plt.show()